Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 5310-5328 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: mulyadi@univpancasila.ac.id Analysis of the effect of financial literacy and financial behavior on the performance of MSMEs Setu Babakan with digital competency as a moderating variable Mulyadi1*, Tyahya Whisnu Hendratni2, Harimurti Wulandjani3 1,2,3Universitas Pancasila, Indonesia; mulyadi@univpancasila.ac.id (M.) Abstract: This study aims to analyze the effect of financial literacy and financial behavior on the performance of MSMEs in Setu Babakan, with digital competence as a moderating variable. MSMEs have an important role in the Indonesian economy, especially in creating jobs and supporting the local economy. In the Setu Babakan area, MSMEs face challenges such as limited digital literacy, business capital, and weak financial management. This research uses a survey method with a quantitative approach, collecting data from 60 MSME respondents, and testing hypotheses using the SEM-PLS (Partial Least Square) model. The results showed that financial literacy does not significantly influence the performance of MSMEs, while financial behavior has a positive effect on performance. Digital competency is proven to improve MSME performance, but cannot moderate the relationship between financial literacy and behavior with MSME performance. The implications of this study indicate the importance of financial behavior training and strengthening digital competencies for MSMEs. These results can be a reference for regional managers and universities to support digital competency training and financial management for MSME actors, so their performance can be more optimal in facing business competition. Keywords: Digital competency, Financial behavior, Financial literacy, MSMEs, Organizational performance. 1. Introduction MSMEs (Micro, Small, and Medium Enterprises) have a very important role in the country's economy. MSMEs are able to contribute to the country's GDP, reduce poverty, and help in the absorption of labor. The existence of MSMEs in Indonesia is the foundation of some Indonesian people to get income (Fitria et al., 2021). Setu Babakan is one of the interesting attractions for tourists who want to enjoy something typical of the countryside or witness the original Betawi culture firsthand. Setu Babakan, apart from being a typical rural natural environment and cultural area, in this village there are also many Betawi specialties served by MSMEs who are assisted by the Setu Babakan area manager and MSMEs around the Setu Babakan area. Based on sources from BPS South Jakarta City (2023) in an annual report, it provides an overview of the economic conditions and challenges faced by MSMEs in the South Jakarta area, including Setu Babakan. MSMEs in Setu Babakan face several performance issues that hinder their development and growth. Several key issues are often faced by MSMEs in this area. First, the lack of digital literacy means that many MSMEs in Setu Babakan still have limitations in the use of digital technology. Low digital literacy makes it difficult for them to utilize online platforms for more effective marketing, sales, and business management. Secondly, business capital is limited, which means capital issues are often a major obstacle for MSMEs in Setu Babakan. Access to financing is still limited, and many businesses do not have enough capital to expand their business or improve the quality of products and services. Third, weak financial management, many MSMEs in Setu Babakan are still lacking in terms of business and 5311 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate financial management. Without a good management system, businesses find it difficult to thrive and face market challenges. This includes disorganized financial management and lack of good record-keeping. Fourth, limited market access means that MSMEs in Setu Babakan often have limited market access. They struggle to reach a wider market outside of their area, resulting in limited opportunities for increased revenue and business growth. Fifth, the quality of products and services means that the quality of products and services offered by MSMEs in Setu Babakan is sometimes less competitive. This is due to a lack of training and knowledge of the quality standards required by the wider market. Sixth, bureaucracy and regulation, bureaucratic and regulatory constraints are also a challenge for MSMEs in Setu Babakan. Complicated licensing processes and frequently changing regulations can make it difficult for businesses to operate legally and efficiently. By understanding and addressing these issues, MSMEs in Setu Babakan can improve their performance and contribute more significantly to the local economy. Efforts such as improving digital literacy, access to finance, and business management training can be an important first step. Improving MSME performance can encourage MSMEs to survive, be able to compete with other businesses, and not experience bankruptcy. However, due to their limited knowledge of understanding basic financial concepts, MSME actors cannot make decisions related to financial management appropriately (Suindari & Juniariani, 2020). (Kasenda & Wijayangka, 2019) state that performance is an achievement obtained from a person or company or organization in achieving a goal. The best performance is the main expectation of a business unit in carrying out its operational activities. Performance is the success of a personnel, team, or organizational unit in achieving strategic goals or long-term goals that have been previously set with the expected behavior. Good performance in all areas of production, marketing, finance, and distribution is an absolute requirement for organizations to continue to exist. Good organizational performance is expected to be stronger to become the backbone of the economy which will increasingly play a role in the national economy. Business people must be able to improve their abilities, both in the form of technical skills (knowledge of accounting and financial statements) and managerial skills in order to run the business they run. 1.1. State of The Art Thestate of the art in this study was generated by summarizing the main findings of previous studies. Some important points that are part of the state of the art in this study. Previous research conducted by (Lestari, 2013), showed that psychological factors such as religiosity, risk perception, gender cannot influence individual investment decisions. Meanwhile, (Idawati & Pratama, 2020), Kasendra & Wijayangka (2019) financial literacy has a positive effect on the performance and sustainability of MSMEs. Previous research conducted by (Pramestiningrum, 2019) and (Septiani & Wuryani, 2020) found that the level of financial literacy affects business performance. The researchers highlighted the importance of understanding financial information in making better investment decisions. Previous research conducted by (Fitria et al., 2021) shows that financial literacy and financial behavior have no significant effect on the performance of MSMEs while financial attitudes have a positive and significant effect on the performance of MSMEs. Previous research conducted by Rustam (2021) highlighted that Financial Literacy, Financial Behavior and Financial Attitudes have a positive effect on Investment Decision. Financial Literacy, Financial Behavior and Financial Attitudes have a negative effect on Firm Bankruptcy. Research that has been proposed as State of the Art can be observed that previous research did not holistically and comprehensively discuss all related variables, namely Financial Literacy, Financial Behavior and Digital Competency. Previous research tends to only discuss some of these variables in relation to MSME performance. In other words, this research not only overcomes the limitations of previous studies in the scope of the variables discussed, but also opens new doors to explore aspects not previously explored. This provides significant added value to the understanding of academics and practitioners about the factors 5312 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate that influence Organizational Performance, as well as the relevance of this research in facing the real challenges faced by MSMEs in the Setu Babakan Area, South Jakarta. 2. Methods This study aims to empirically test and analyze Financial Literacy, Financial Behavior on the performance of Setu Babakan MSMEs with Digital Competency as a moderating variable. This research design is a causal research design (causal studies) where this research intends to examine the influence between variables. This research is designed using a hypothesis and is an explanation of the object under study (explanatory research). Research hypothesis testing was carried out using the Structural Equation Model (SEM) approach using Smart Partial Least Square (SmartPLS) software. 2.1. Variable Operationalization 1. Organizational Performance Variable (Y = Dependent Variable) 2. Financial Literacy Variable (X1 = Independent Variable ) 3. Financial Behavior Variable (X2 = Independent Variable) 4. Digital Competency Variable (Z = Moderation Variable) The population in this study are MSMEs in the Setu Babakan area with a total of 96 MSMEs. Meanwhile, the guidelines for the number of samples used in this study follow the sample formula according to Rao Purba (1996) which tests Consumer Perception as follows: n = N / (1 + N. Moe2) where: n = Sample Quantity N = Total Population Moe = Margin of Error In this situation, a margin of error level of 10% would result in 48.97 respondents with the following calculation: n = 96 / (1 + 96 x 0,102) n = 96 / (1 + 0,96) n = 96 / (1,96) n = 48,97 respondents = 50 respondents In this study, researchers took a sample of 60 respondents with the aim of meeting the limits of the ideal number of research samples with the Structural Equation Model (SEM) approach using Smart Partial Least Square (SmartPLS) software. This research based on the source is primary data. Respondents will answer the questions used to obtain primary data by selecting the answers that have been provided on a Likert scale. The Likert scale is a scale that provides a score of 1-5 to determine the degree of respondent to a series of questions contained in the questionnaire. 3. Result and Discussion 3.1. Profile of MSME Respondents in the Setu Babakan Area Setu Babakan is a Village Area located in the suburbs of South Jakarta, precisely on Muhammad Kahfi II Street, Srengseng Sawah Village, Jagakarsa District covering an area of 289 hectares, 65 hectares of which are owned by the government of which only 32 hectares have been managed. Based on the decree of the DKI Jakarta regional government dated January 20, 2020, the area became the Betawi Cultural Village (PBB) which is a place for the preservation and development of Betawi culture. Here you can watch performances that are nuanced with authentic Betawi culture such as Betawi lenong art, Betawi dance, traditional gambang kromong music and other art performances. In addition, there are many Betawi culinary specialties such as kerak telur, selendang mayang, bir pletok and so on that can be enjoyed by visitors. Tourists visiting Setu Babakan can also watch Betawi cultural arts performances that are often staged. In addition, visits by schools and universities are also in the context of research and community 5313 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate service activities in the form of training on management, entrepreneurship and financial management of MSMEs. Based on information obtained by researchers through google form, there are at least hundreds of MSMEs that occupy the Setu Babakan area. Some are MSMEs that are directly fostered by the Setu Babakan area manager and some are MSMEs that are located around Setu Babakan but are not fostered by the Setu Babakan manager. The following profile of 60 MSMEs in the Setu Babakan area can be seen as follows: Table 1. Number of MSMEs by gender. No Gender Total Percentage 1 Male 25 41.67% 2 Female 35 58.33% Total 100% Table 1. shows the number of MSME respondents based on gender in the Setu Babakan area. From the table it is known that the number of men is 25 people (41.67%), the remaining women are 58 people (58.33%). From this table it can be concluded that the majority of business actors in the Setu Babakan area are women. Table 2. Business sector of MSMEs. No Line of business Total Percentage 1 Culinary 42 70.00% 2 Water tourism services 2 3.33% 3 General trade 2 3.33% 4 Handicrafts 2 3.33% 5 Textile/Fashion 3 5.00% 6 Others 9 15.00% Total 60 100% Table 2. shows that the business fields of MSMEs in the Setu Babakan area in South Jakarta are very diverse, based on the culinary category which consists of various kinds of food and drinks such as bir pletok, traditional cakes to frozen food, drinks, a type of fast food and so on which is the largest number, namely (70%). The rest are businesses engaged in general trade, water tourism services and handicrafts each at 3.33% and textiles/fashion at 5% as well as other businesses, in the form of farm produce selling services, suplair, travel and so on as many as 9 MSMEs (15%). Table 3. Length of business. No Length of business Total Percentage 1 < 1 Year 7 11.67% 2 1 - 5 Years 23 38.33% 3 > 5 - 15 Years 19 31.67% 4 >15 - 20 Years 6 10.00% 4 > 20 Years 5 8.33% Total 60 100% Table 3 illustrates the characteristics of respondents based on the length of time the business has been running. Of the 60 people studied, 23 people (38.33%) of them have been in business for 1 - 5 years, 19 people (31.67%) of them have been in business for more than 5 - 15 years, 6 people (10%) of them have been in business for more than 15 years - 20 years, and 5 people (8.33%) of them have been 5314 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate working for more than 20 years. There were only 7 people (11.67) who had been in business for less than one year. Based on this data, it can be concluded that most respondents have worked between more than 1 year and 5 years. 3.2. Descriptive Statistical Analysis of Setu Babakan MSMEs 3.2.1. Respondents' Responses to MSME Business Conditions Table 4. Respondents' responses on MSME business conditions. No Question list Percentage Yes No 1. I am currently a fostered partner of the Setu Babakan area manager. 19 41 2. I still have limitations in using digital technology and have difficulty understanding online platforms. 23 37 3. I have capital problems in running my current business. 37 23 4. I have participated in training on entrepreneurship/business management organized by the area manager/university in Setu Babakan. 26 34 5. I felt that I had limited market access and had difficulty reaching a wider market. 45 15 6. I maintain the quality of my products and services to my customers 58 2 7. I feel that complicated licensing processes and changing regulations make it difficult for my business to operate. 36 24 Total respondent 60 people Table 4. It is known that the number of respondents who have become fostered partners of the Setu Babakan Area manager is 19 people (31.67%), while those who have not become fostered partners of the Setu Babakan Area manager are 34 people (56.67%). The number of respondents who still have limitations in using digital technology and difficulty understanding online platforms is 23 people (38.33%), while those who do not have limitations in using digital technology and difficulty understanding online platforms are 37 people (61.67%) From the table it is also known that the number of respondents who have capital problems in running their current business is still greater as many as 37 people (61.67%). While those who do not have capital problems in running their current business are 23 people (38.33%). The number of respondents who have attended training on entrepreneurship/business management organized by the area manager/university in Setu Babakan is 26 people (43.33%), and those who have never attended training are 34 people (56.67%). This means that those who have never attended training are more than those who have attended training. Respondents who felt they had limited market access and difficulty reaching a wider market were 45 people (75%), while those who stated that they did not feel they had limited market access and difficulty reaching a wider market were satisfied as many as 15 people (25%). This means that there are still far more respondents who have limited market access and difficulty reaching a wider market. Overall the number of respondents maintaining the quality of products and services to customers was 96.67% and only 3.33% were less concerned about maintaining quality. Finally, the number of respondents who stated that they felt that the complicated licensing process and changing regulations made it difficult for their business was 36 people (60%), while the remaining 24 people (40%) stated that they did not feel that the complicated licensing process and changing regulations made it difficult for their business. 5315 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate 3.2.2. Respondents' Responses to the Financial Literacy Variable Table 5. Recapitulation of financial literacy variable responses. No. Dimensions Score earned Max. score % of max. score Category 1 Basic knowledge of financial management 651 900 72.33% Good 2 Insurance 432 600 72.00% Good 3 Savings and loan management 494 600 82.33% Good 4 Investment 458 600 76.33% Good Total score 2035 2700 75.37% Good Table 5. shows respondents' responses about the Financial Literacy variable. From the results of data processing presented in the table, it can be seen that the total score for the Financial Literacy variable is 2035. The total score is entered on a continuum line whose measurement can be done as follows: Maximum Index Value = 5 x 9 x 60 = 2700 Minimum Index Value = 1 x 9 x 60 = 540 Interval Distance = (Max. -Min.) : 5 = (2700 - 540) : 5 = 432 Percentage Score = (Score : Max.) x 100% = (2035 : 2700) x 100% = 75,37 % 75,37% Very Less Less Fair Good Very Good 20% 36% 52% 68% 84% 100% Figure 1. Financial literacy variable continuum line. The ideal expected score for respondents' answers to 9 questions is 2700. Based on the calculations in Table 5, it shows that the value obtained is 2035 or 75.37% of the ideal score. Thus it can be concluded that the Financial Literacy variable is in the good category. Meanwhile, the indicator that obtained the highest answer came from the Savings and Loan Management Dimension on the Third Indicator which states I understand the use of separate funds that are not used for other purposes and I know about the benefits of the risks of savings and loans with a score obtained of 82.33%. Table 6. Recapitulation of financial behavior variable responses. No Dimensions Score earned Max. score % of max. score Category 1 Obsession 693 900 77.00% Good 2 Power 478 600 79.66% Good 3 Effort 423 600 70.50% Good 4 Inadequacy 715 900 79.44% Good 5 Retention 468 600 78.00% Good 6 Security 720 900 80.00% Good Total score 3497 4500 77.71% Good 5316 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate 3.2.3. Respondents' Responses to the Financial Behavior Variable Table 6. shows respondents' responses about the Financial Behavior variable. From the results of data processing presented in the table, it can be seen that the total score for the Financial Behavior variable is 3497. The total score is entered on a continuum line whose measurement can be done as follows: Maximum Index Value = 5 x 15 x 60 = 4500 Minimum Index Value = 1 x 15 x 60 = 900 Interval Distance = (Max. -Min.) : 5 = (4500 - 900) : 5 = 720 Percentage Score = (Score: Max.) x 100% = (3497 : 4500) x 100% = 77,71% 77,71% Very Less Less Fair Good Very Good 20% 36% 52% 68% 84% 100% Figure 2. Financial behavior variable continuum line. The ideal expected score for respondents' answers to 15 questions is 4500. Based on the calculations in table 6, it shows that the value obtained is 3497 or 77.71% of the ideal score. Thus it can be concluded that the Financial Behavior variable is in the good category. Meanwhile, the indicator that received the highest answer came from the Security Dimension on the Third Indicator which states I believe that my business has control over the financial situation in terms of strength with a score obtained of 81.00%. Table 7. Recapitulation of digital competency variable responses. No Dimensions Score earned Max score. % of max score Category 1 Information and data literacy 685 900 76.11% Good 2 Communicatian and collaboration 696 900 77.33% Good 3 Digital content creation 694 900 77.11% Good 4 Safety 724 900 80.44% Good 5 Project solving 711 900 79.00% Good Total score 3510 4500 78.00% Good 3.2.4. Respondents' Responses to the Digital Competency Variable Table 7 shows respondents' responses about the Digital Competency variable. From the results of data processing presented in the table, it can be seen that the total score for the Digital Competency variable is 3510. The total score is entered on a continuum line whose measurement can be done as follows: Maximum Index Value = 5 x 15 x 60 = 4500 Minimum Index Value = 1 x 15 x 60 = 900 Interval Distance = (Max. - Min.) : 5 = (4500 - 900) : 5 = 720 Percentage Score = (Score : Max.) x 100% = (3510 : 4500) x 100% = 78,00% 78% 5317 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate Very less Less Good enough Good Very good 20% 36% 52% 68% 84% 100% Figure 3. Digital competency variable continuum line. The ideal expected score for respondents' answers to 15 questions is 4500. Based on the calculations in table 7, it shows that the value obtained is 3510 or 78.00% of the ideal score. Thus it can be concluded that the Digital Competency variable is in the good category. Meanwhile, the indicator that received the highest answer came from the Safety Dimension which states that I realize the importance of safety and psychological protection in using digital technology with a score of 83.33%. 3.2.5. Respondents' Responses to Organization Performance Variables Table 8. Recapitulation of variable responses organization performance. No Dimensions Score earned Max score. % of max score. Category 1 Stakeholders and financial perspective 672 900 74.67% Good 2 Customer perspective 724 900 80.44% Good 3 Internal process perspective 693 900 77.00% Good 4 Innovation and learning perspective 689 900 76.55% Good Total score 2778 3600 77.16% Good Table 8 shows respondents' responses about the Organization Performance variable. From the results of data processing presented in the table, it can be seen that the total score for the Organization Performance variable is 2778. The total score is entered on a continuum line whose measurement can be done as follows: Maximum Index Value = 5 x 12 x 60 = 3600 Minimum Index Value = 1 x 12 x 60 = 720 Interval Distance = (Max. -Min.) : 5 = (3600 - 720) : 5 = 576 Percentage Score = (Score: Max.) x 100% = (2778 : 3600) x 100% = 77,16% 77,16% Very less Less Good enough Good Very good 20% 36% 52% 68% 84% 100% Figure 4. Organization performance variable continuum line. The ideal expected score for respondents' answers to 12 questions is 3600. Based on the calculations in Table 8, it shows that the value obtained is 2778 or 77.16% of the ideal score. Thus it can be concluded that the Organization Performance variable is in the good category. Meanwhile, the indicator that received the highest answer came from the Customer Perspective Dimension on the First Indicator which states My customers are generally satisfied with the products/services I provide with the score obtained of 83.00%. Descriptive analysis using a continuum line approach to the four variables is a simple technique for mapping respondents' opinions or interpretations of the four variables studied with relevant questions. The scores obtained from all variables are a temporary consideration of the direction of respondents' opinions. Meanwhile, the highest scores of the dimensions of the research variables are as follows: 5318 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate Financial Literacy variables are indicated by Savings and Loan Management (with a score of 83.22%), Financial Behavior variables are indicated by the Security Dimension (with a score of 80.00%), Digital Competency variables are indicated by the Safety Dimension (with a score of 80.44%), and Organization Performance variables are indicated by the Customer Perspective Dimension (with a score of 80.44%). 3.3. Analysis Structural Equation Model-Partial Least Square (SEM-PLS) In this study there are 4 latent variables and 51 manifest variables (indicators). Testing the results of Structural Equation Modeling (SEM) with the Partial Least Square (PLS) approach is done by looking at the results of the measurement model (Outer Model) and the results of the structural model (Inner Model) of the model under study. 3.3.1. Testing the Measurement Model (Outer Model) Convergent Validity relates to the principle that the manifest variables of a construct should be highly correlated. Convergent Validity with PLS software can be seen from the loading factor for each construct indicator, as for assessing Convergent Validity the loading factor value must be more than 0.70 and the Average Extracted (AVE) and communality values must be greater than 0.5, the following results are obtained: Table 9. Final loading factor. Variable (Symbol) Dimensions Indicator Loading factor Note Financial literacy (X1) Basic knowledge of financial management X1.3 0.840 Valid Insurance X1.5 0.750 Valid Savings and loan management X1.6 0.711 Valid X1.7 0.832 Valid Investment X1.8 0,794 Valid X1.9 0,723 Valid Financial behavior (X2) Obsession X2.1 0.715 Valid Power X2.5 0.748 Valid Effort X2.7 0.784 Valid Incompetence X2.8 0,828 Valid X2.9 0,793 Valid X2.10 0,813 Valid Retention X2.11 0,803 Valid X2.12 0,824 Valid Security X2.13 0,850 Valid X2.14 0,841 Valid X2.15 0,816 Valid Digital competency (Z) Information and data literacy Z1 0.848 Valid Z2 0.844 Valid Z3 0.824 Valid Communicatian and collaboration Z4 0.844 Valid Z5 0,781 Valid Z6 0,854 Valid Digital content creation Z7 0,830 Valid Z8 0,889 Valid Z9 0,899 Valid Safety Z10 0,925 Valid Z11 0,790 Valid 5319 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate Project solving Z13 0.838 Valid Z14 0,841 Valid Z15 0,843 Valid Organization performance (Y) Stakeholders and financial perspective Y1 0.836 Valid Y2 0.832 Valid Y3 0,803 Valid Customer perspective Y4 0.784 Valid Y5 0.777 Valid Y6 0,828 Valid Internal process perspective Y7 0.817 Valid Y8 0.797 Valid Y9 0.825 Valid Innovation and learning perspective Y10 0.797 Valid Y11 0.840 Valid Y12 0.851 Valid Table 9. provides information about the Loading Factor value for each manifest variable, the Loading Factor value of all indicators on latent variables shows> 0.70 so that the indicator is declared valid. Table 10. Average variance extracted (AVE). Variable Average variance extracted (AVE) FL 0.642 FB 0.657 DC 0.720 OP 0.666 Figure 5. Reestimated loading factor. In Table 10. it can be seen that the four latent variables have an AVE value greater than the specified value of 0.5 so that all variables are declared valid in explaining their latent variables which indicates that the use of these manifest variables meets the AVE requirements. 5320 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate All manifest variables are declared to have met the Convergent Validity requirements. Convergent Validity itself is validity that is proven if the scores obtained by instruments that measure concepts or measure concepts with different methods have a high correlation. 3.3.2. Discriminant Validity Discriminant Validity can be seen from the measurement of the Cross Loading Factor with the construct and the comparison of AVE with the latent variable correlation. If the construct correlation with the main measurement (each indicator) is greater than the size of the other constructs, it is said that the variable has high discriminant validity. The Cross Loading value is presented as follows: Table 11. Cross loading factor. Variable Indicator FL FB DC OP FL X1.3 0.810 0.683 0.645 0.611 X1.5 0.789 0.767 0.705 0.669 X1.6 0.774 0.606 0.727 0.602 X1.7 0.878 0.717 0.777 0.553 X1.8 0.787 0.678 0.710 0.752 X1.9 0.766 0.724 0.630 0.665 FB X2.1 0.705 0.706 0.543 0.511 X2.5 0.588 0.742 0.671 0.543 X2.7 0.590 0.802 0.597 0.668 X2.8 0.711 0.843 0.857 0.302 X2.9 0.661 0.813 0.413 0.773 X2.10 0.695 0.825 0.607 0.658 X2.11 0.656 0.771 0.578 0.645 X2.12 0.826 0.852 0.686 0.797 X2.13 0.610 0.849 0.899 0.445 X2.14 0.833 0.860 0.784 0.425 X2.15 0.622 0.839 0.865 0.559 DC Z1 0.636 0.665 0.859 0.586 Z2 0.759 0.847 0.854 0.619 Z3 0.731 0.805 0.831 0.578 Z4 0.721 0.595 0.842 0.331 Z5 0.870 0.714 0.777 0.538 Z6 0.653 0.709 0.849 0.374 Z7 0.693 0.602 0.835 0.574 Z8 0.638 0.701 0.897 0.486 Z9 0.574 0.601 0.906 0.454 Z10 0.563 0.716 0.926 0.590 Z11 0.515 0.681 0.780 0.339 Z13 0.678 0.838 0.838 0.495 Z14 0.678 0.827 0.834 0.576 Z15 0.610 0.592 0.842 0.474 Y1 0.704 0.521 0.538 0.836 Y2 0.648 0.579 0.444 0.831 Y3 0.647 0.533 0.491 0.802 Y4 0.747 0.742 0.629 0.786 Y5 0.704 0.521 0.538 0.779 Y6 0.581 0.360 0.452 0.829 5321 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate Y7 0.680 0.400 0.267 0.817 Y8 0.671 0.722 0.660 0.798 Y9 0.559 0.466 0.386 0.825 Y10 0.747 0.742 0.629 0.796 Y11 0.498 0.480 0.267 0.839 Y12 0.605 0.515 0.429 0.851 Based on Table 11, it can be seen that the Cross Loading Factor correlation value of each latent construct for the corresponding indicator is higher than other constructs, so it can be concluded that the indicators used to measure latent variables have met the requirements. 3.3.3. Reliability Test Reliability tests in Partial Least Square (PLS) can use two methods, namely Composite Reliability (CR) and Cronbach's Alpha (CA) which are presented as follows: Table 12. Reliability test results. Cronbach’s alpha Composite reliability Conclusion Financial literacy (FL) 0.888 0.915 Reliable Financial behavior (FB) 0.947 0.955 Reliable Digital competency (DC) 0.970 0.973 Reliable Organization performance (OP) 0.954 0.960 Reliable From the test results it can be seen that the Composite Reliability (CR) value is greater than 0.7, and the Cronbach's Alpha (CA) value is greater than 0.6, so it can be concluded that the data is reliable which indicates that all indicators have consistency in measuring each variable. 3.3.4. Structural Model Testing (Inner Model) This structural model measurement is to test the effect of one latent variable on other latent variables. Testing is done by looking at the path value to see whether the effect is significant or not seen from the t value of the path value (t value can be obtained by doing Boothstraping). The following is a picture of the bootstrapping results conducted in this study: Figure 6. Bootstrapping. 5322 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate 3.3.5. Hypothesis Testing The hypothesis in this study will be tested using the path coefficient value and t values to see whether there is a significant effect or not. In addition, the results of the path significance test also show the parameter coefficient value (Original sample). The parameter coefficient shows the significant value of the effect of each research variable. Table 13. Path significance test. Variable Original sample (O) Sample mean (M) Standard deviation (STDEV) t Statistics (O/STDEV) P values FL → OP -0.083 -0.072 0.113 0.740 0.459 FB → OP 0.415 0.419 0.164 2.531 0.011 DC → OP 0.635 0.623 0.121 5.259 0.000 DC x FL → OP -0.088 -0.094 0.132 0.663 0.507 DC x FB → OP 0.116 0.114 0.119 0.981 0.327 Table 14. Hypothesis test matrix. H Variable Correlation t- value t- table Description H1 Financial literacy → Organization performance -0.083 0.740 1,96 No effect H2 Financial behavior → Organization performance 0.415 2.531 1,96 Positively affected H3 Digital competency → Organization performance 0.635 5.259 1,96 Positively affected H4 Digital competency x Financial literacy → Organization performance -0.088 0.663 1,96 No effect H5 Digital competency x Financial behavior → Organization performance 0.116 0.981 1,96 No effect In this study, researchers used a confidence level of 95%. The Path Coefficient score indicated by the t-statistic value must be above 1.96 for a two-tailed hypothesis. Based on the Path Coefficient and t- statistic in the table, the following conclusions can be drawn: 3.4. Effect of Financial Literacy on Investment Decision H0: Financial Literacy has no significant effect on Organization Performance H1: Financial Literacy has a significant effect on Organizational Performance Reject H0 and accept H1 if t-value > t-table To test the above hypothesis, the t-value is used to see the effect of Financial Literacy on Organization Performance with a t-value of 0.740, this value is smaller than 1.96 with a=0.05, so it can be concluded that H0 is accepted, meaning that there is no significant effect of Financial Literacy and 5323 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate Organization Performance. The Financial Literacy variable on Organization Performance has an original sample of -0.083 with a negative direction, meaning that the better the Financial Literacy, the Organization Performance will decrease by 0.083. 3.5. The Effect of Financial Behavior on Investment Decision H0: Financial Behavior has no significant effect on organizational performance H1: Financial Behavior has a significant effect on Organization Performance Reject H0 and accept H1 if t-value > t-table To test the above hypothesis, the t value is used to see the effect of Financial Behavior on Organization Performance with a t value of 2.531, this value is greater than 1.96 with a=0.05, so it can be concluded that H1 is accepted, meaning that there is a significant effect of Financial Behavior on Organization Performance. The Financial Behavior variable on Organization Performance has an original sample of 0.415 with a positive direction, meaning that the better the Financial Behavior, the greater the Organization Performance will be 0.415. 3.6. Effect of Digital Competency on Organizational Performance H0: Digital Competency has no significant effect on Organization Performance H1: Digital Competency has a significant effect on Organizational Performance Reject H0 and accept H1 if t-value > t-table To test the above hypothesis, the t-value is used to see the effect of Digital Competency on Organization Performance with a t-value of 5.295, this value is greater than 1.96 with a=0.05 so it can be concluded that H1 is accepted, meaning that there is a significant effect of Digital Competency on Organization Performance. The Digital Competency variable on Organization Performance has an original sample of 0.635 with a positive direction, meaning that the better the Digital Competency, the Organization Performance will also increase by 0.635. 3.7. Moderation of Digital Competency on Financial Literacy on Organization Performance H0: Digital Competency in Financial Literacy does not significantly moderate organizational performance H1: Digital Competency in Financial Literacy moderates significantly on Organization Performance Reject H0 and accept H1 if t-value > t-table To test the above hypothesis, the t-value is used to see the effect of Digital competency on Financial Literacy on Organization Performance with a t-value of 0.663, this value is smaller than 1.96 with a=0.05 so it can be concluded that H0 is accepted, meaning that Digital Competency on Financial Literacy does not moderate significantly on Organization Performance. The Digital Compentency variable on Financial Literacy on Organization Performance has an original sample of -0.088 with a negative direction, meaning that the greater the Digital Compentency on Financial Literacy, the more Organization Performance will decrease by 0.088. 3.8. Moderating Digital Competence on Financial Behavior to Organizational Performance H0: Digital Competency in Financial Behavior does not significantly moderate organizational performance H1: Digital Competency on Financial Behavior Significantly Moderates Organization Performance Reject H0 and accept H1 if t-value > t-table To test the above hypothesis, the t-value is used to see the effect of Digital competency on Financial Behavior on Organization Performance with a t-value of 0.981, this value is smaller than 1.96 with a=0.05 so it can be concluded that H0 is accepted, meaning that Digital Competency on Financial Behavior does not moderate significantly on Organization Performance. The Digital Compentency variable on Financial Behavior on Organization Performance has an original sample of 0.116 with a positive direction, meaning that the greater the Digital Compentency on Financial Behavior, the more Organization Performance will increase by 0.116. 5324 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate 3.9. R Square Test The influence of the dependent variable can be displayed by the R square value. The following is the acquisition of the R Square value. Table 15. R square results. Adjusted R square Organization performance (OP) 0.861 Through the coefficient of determination (R Square) value contained in Table 15. it can be seen in the first sub-structure that the R Square value of the Organization Performance (OP) variable is 0.861, which indicates that the Organization Performance (OP) variable can be explained by 86.1% by the Financial Literacy (FL), Financial Behavior (FB), and Digital Competency (DC) variables. 3.10. Predictive Relevance Q Square measures how well the observed values are generated by the model as well as its parameter estimates. A Q Square value greater than 0 (zero) shows that the model has predictive relevance, while a Q Square value of less than 0 (zero) shows that the model has no predictive relevance. To calculate Q2, the following formula can be used: 𝑄2 = 1- (1-R12) 𝑄2 = 1- (1-0,861) 𝑄2 = 0,861 The achieved 𝑄2value of 0.861 means that 𝑄2 above zero provides evidence that the model has Predictive Relevance. 3.11. Direct Effect and Moderating Effect Relationship Table 16. Direct and moderation effect. Direct influence Moderating effect FL → OP -0.083 FB → OP 0.415 DC → OP 0.635 DC x FL → OP -0.088 DC x FB → OP 0.116 Based on Table 16, it is known that from testing the moderating effect of Digital Competency on Financial Literacy and Financial Behavior on Organization Performance, there is no greater influence when compared to the direct effect of Digital Competency on Organization Performance. This is supported by a t-statistic smaller than 1.96 or a p value greater than 0.05 which can be seen in Table 13. Financial Literacy is the ability of an individual or organization to understand basic financial concepts such as savings, investment, and budget management. However, a high level of literacy does not necessarily result in better investment decisions as external factors such as market conditions and economic dynamics also play an important role in organizational investments. Therefore, financial literacy alone cannot be considered a key determinant of organizational performance. Financial Behavior encompasses the way an individual or organization manages the organization's finances, including spending, investing, and other financial decisions. While financial mismanagement can have a negative impact on organizational performance, other factors such as business strategy and changing market conditions also affect investment returns. Therefore, financial behavior is not always the only determinant in relation to organizational performance. 5325 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate Overall, Financial Literacy, Financial Behavior, and Digital Competency are important elements in organizational financial decision-making. However, the resulting direct influence on organizational performance is often complex and dependent on various other factors that must be considered in a more in-depth analysis. 4. Discussion of Findings Based on the results of statistical data processing presented as the basis for answering the formulation of this research problem, each hypothesis can be systematically explained as follows: 4.1. The Effect of Financial Literacy on Organizational Performance The results showed that Financial Literacy has no effect on organizational performance. In this case, it is known that the research results show a high level of Financial Literacy, but not higher than other variables. Many MSMEs in Setu Babakan still rely on traditional business models that may not fully rely on formal financial knowledge. Some MSMEs in Setu Babakan are family businesses that prioritize social relationships over profitability, so financial literacy may not be a top priority. Setu Babakan MSMEs can cope by relying on a deep understanding of financial and investment aspects due to the relatively small business capacity. Thus, individual or group Financial Literacy is not the main determining factor in determining the organizational performance of Setu Babakan MSMEs. For large companies, organizational performance is influenced by strong managerial factors and business strategies. Good managerial skills, such as effective planning, organizing, and controlling abilities can have a greater impact on organizational performance than the level of Financial Literacy of individuals or groups. In addition, organizational success is also determined by the right business strategy and good implementation. If the organization has a strong business strategy and is able to implement it well, the level of individual or group Financial Literacy becomes a less significant factor in determining organizational performance. Financial Literacy can be improved through proper education and training. If organizations recognize the importance of Financial Literacy and make efforts to improve the financial understanding of individuals or groups within them, the impact of Financial Literacy on organizational performance can be strengthened. Through financial training, teaching financial management, or consulting with financial experts, individuals or groups within the organization can improve their ability to manage finances and make smart investment decisions. It is important for organizations to consider these factors and adopt appropriate measures to ensure optimal organizational performance. The results of this study provide full support to previous research by (Fitria et al., 2021) and research by (Mulyadi et al., 2023) which states that Financial Literacy has no effect on organizational performance. The results of this study refute research conducted by (Kasenda & Wijayangka, 2019), (Idawati & Pratama, 2020), (Pramestiningrum, 2019), and (Septiani & Wuryani, 2020) showing Financial Literacy has a positive effect on Organization Performance. 4.2. The Effect of Financial Behavior on Organizational Performance The results showed that Financial Behavior has a positive effect on organizational performance. One of the reasons Financial Behavior has a significant effect on organizational performance is when Setu Babakan MSMEs have implemented good internal control within a limited scope of business. With good financial management, Setu Babakan MSMEs can minimize the risk of adverse financial behavior. For example, by adopting controlled monitoring and reporting mechanisms, organizations can identify and address inappropriate or adverse actions in a timely manner. The influence of Financial Behavior on organizational performance can be suppressed if the organization has a strong and focused business strategy. If the organization has a clear strategic direction, well-defined goals, and a mature implementation plan, then organizational performance is more likely to be influenced by the successful implementation of the strategy rather than individual or group financial behavior. In this case, the success of the organization depends more on the fulfillment of an effective business strategy than on financial behavioral factors. 5326 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate It is important for Setu Babakan MSMEs to develop effective control mechanisms, pay attention to relevant external factors, and have a strong business strategy to ensure their business performance remains optimal. The results of this study provide full support to previous research by (Rustan, 2021) which states that Financial Behavior affects Organization Performance. Meanwhile, other research conducted by (Fitria et al., 2021) and (Mulyadi et al., 2023) states that Financial Behavior has no effect on Organization Performance. 4.3. Effect of Digital Competency on Organizational Performance The results showed that Digital Competency has a positive effect on organizational performance. When individuals or groups within an organization have a good understanding of the digital capabilities at hand and are able to effectively evaluate the benefits, this can help MSMEs in Setu Babakan make better decisions, manage digital capabilities more efficiently, and achieve better performance overall. Digital Competencies include information and data literacy, communication and collaboration, digital content creation, security, and problem-solving projects. Now that digital skills have been recognized in the digital era, the benefits of digital competency are vast. An important part of the nature of learning is how Setu Babakan MSMEs utilize learning assets, as technology develops in today's digitalized world, Setu Babakan MSMEs develop their skills to utilize digital technology in their business development. The most popular thing today is the use of social media to market their products. Such as WhatsApp, Instagram, Facebook, and so on. This study supports research conducted by (Liesander & Diah Dharmayanti, 2017); (Marguna, 2020); and (Andriana & Ardi, 2022) which states that digital competency has a direct effect on organizational performance. 4.4. Moderation of Digital Competency on Financial Literacy on Organization Performance The results showed that Digital Competency on Financial Literacy on Organization Performance had no significant effect. In other words, Digital Competency cannot moderate Financial Literacy on Organization Performance. This can happen because the direct effect of Digital Competency on Organization Performance (UMKM Setu Babakan performance) is very strong. Digital competence is a moderating variable, namely a variable that can strengthen or weaken the direct relationship between the independent variable, namely Financial Literacy with the dependent variable, namely Organization Performance. This means that the Digital Competency variable cannot moderate the Financial Leteracy variable on the Organization Performance variable. 4.5. Moderation of Digital Competency on Financial Behavior on Organization Performance The results showed that Digital Competency on Financial Behavior on Organization Performance had no significant effect. In other words, Digital Competency cannot moderate Financial Behavior on Organization Performance. This can happen because the direct effect of Digital Competency on Organization Performance (performance of Setu Babakan MSMEs) is very strong. The same thing can be said that Digital competence is a moderating variable, namely a variable that can strengthen or weaken the direct relationship between the independent variable, namely Financial literacy with the dependent variable, namely Organization Performance. So in this case it means that the Digital Competency variable cannot moderate the Financial Behavior variable on the Organization Performance variable. 5. Conclusions and Suggestions Based on the results of the analysis and evaluation of research data, it is concluded that Financial Literacy does not have a significant effect on the organizational performance of Setu Babakan MSMEs, which may be due to the small scale of the business and the lack of adequate managerial skills. On the other hand, Financial Behavior has a positive influence on investment decisions, because good financial behavior encourages more rational investment decision-making. Digital Competency plays a positive role in improving organizational performance, especially in the use of digital technology such as social 5327 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5310-5328, 2024 DOI: 10.55214/25768484.v8i6.3176 © 2024 by the authors; licensee Learning Gate media for marketing. However, Digital Competency is not able to moderate the relationship between Financial Literacy and Financial Behavior with organizational performance, because the direct influence of digital capabilities on MSME performance is strong enough. 5.1. Advice For the Setu Babakan Area Manager, based on the survey results, 60% of MSMEs feel that the complicated licensing process and frequently changing regulations make it difficult for them. The manager is expected to simplify the licensing process. In addition, 75% of MSMEs claimed to have difficulties in reaching a wider market, so efforts are needed from managers to help overcome this limited market access. For universities, 38% of MSMEs stated that they still experience difficulties in using digital technology and online platforms. It is expected that universities can work together with area managers to provide digital competency training and capital management through community service activities. From a theoretical perspective, this study developed a questionnaire to evaluate the factors affecting the performance of Setu Babakan MSMEs, and future researchers can utilize or develop this questionnaire for further research, including testing other variables beyond financial literacy, financial behavior, and digital competence. Copyright: © 2024 by the authors. 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